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Transforms & Theming

Transforms

Transforms derive new data from an existing layer before rendering. You specify a transform with the transform argument in addIoLayer().

Available Transforms

Transform Description Used by
"identity" Default. Passes data through unchanged. All types
"lm" Fits a linear model and returns fitted values. line
"loess" LOESS non-linear smoothing. Options: span, degree, n_grid. line
"polynomial" Polynomial regression. Options: degree, n_grid. line
"ci" Confidence or prediction interval band. Options: method, level, interval. area
"smooth" Simple or exponential moving average. Options: method ("sma"/"ema"), window, alpha. line
"mean" Group mean aggregation. point, bar
"mean_ci" Group mean ± confidence interval. Options: level, method ("t"/"se"). rangeBar
"residuals" Regression residuals vs. fitted values. Options: method. point
"summary" General aggregation. Options: stat ("count"/"sum"/"sd"/"var"/"min"/"max"). point, bar
"cumulative" Computes running totals with base/cumulative columns. waterfall (auto-applied)
"quantiles" Computes Q1, median, Q3, whisker bounds per group. boxplot (internal)
"median" Computes group medians. boxplot, violin (internal)
"outliers" Returns rows beyond 1.5x IQR fences. boxplot (internal)
"density" Kernel density estimation via stats::density(). violin, ridgeline (internal)

Transforms marked “internal” are applied automatically by composite chart types. The first 10 transforms are user-facing — use them directly in addIoLayer().

Linear Model Trend Line

Add a trend line to a scatter plot by combining a "point" layer with a "line" layer that uses transform = "lm":

myIO() |>
  addIoLayer(
    type = "point",
    color = "#E69F00",
    label = "points",
    data = mtcars,
    mapping = list(x_var = "wt", y_var = "mpg")
  ) |>
  addIoLayer(
    type = "line",
    transform = "lm",
    color = "red",
    label = "trend",
    data = mtcars,
    mapping = list(x_var = "wt", y_var = "mpg")
  )

The lm transform fits y ~ x using stats::lm() and replaces the y values with the fitted values. The resulting line is sorted by x so it renders as a smooth trend.

Cumulative Transform (Waterfall)

The "cumulative" transform is auto-applied when you use type = "waterfall". It computes running totals and produces _base_y and _cumulative_y columns that the waterfall renderer uses for floating bars:

df <- data.frame(
  step  = c("Start", "Sales", "Returns", "Total"),
  value = c(100, 50, -20, NA),
  is_total = c(FALSE, FALSE, FALSE, TRUE)
)

myIO() |>
  addIoLayer(
    type = "waterfall",
    label = "bridge",
    data = df,
    mapping = list(x_var = "step", y_var = "value", total = "is_total")
  ) |>
  defineCategoricalAxis(xAxis = TRUE)

Confidence Interval Band

Overlay a confidence interval on a scatter plot:

myIO(data = mtcars) |>
  addIoLayer(type = "point", color = "#4E79A7", label = "Data",
    mapping = list(x_var = "wt", y_var = "mpg")) |>
  addIoLayer(type = "line", color = "#E15759", label = "Trend",
    transform = "lm",
    mapping = list(x_var = "wt", y_var = "mpg")) |>
  addIoLayer(type = "area", color = "#E15759", label = "95% CI",
    transform = "ci",
    mapping = list(x_var = "wt", y_var = "mpg"),
    options = list(level = 0.95))

The ci transform accepts method ("lm" or "loess"), level (default 0.95), and interval ("confidence" or "prediction"). The mapping only needs x_var and y_var — low_y/high_y are auto-injected.

LOESS Smoothing

Non-linear smoothing with adjustable span:

myIO(data = mtcars) |>
  addIoLayer(type = "point", color = "#4E79A7", label = "Data",
    mapping = list(x_var = "wt", y_var = "mpg")) |>
  addIoLayer(type = "line", color = "#E15759", label = "LOESS",
    transform = "loess",
    mapping = list(x_var = "wt", y_var = "mpg"),
    options = list(span = 0.5))

Mean ± CI Error Bars

Group means with confidence intervals:

myIO(data = iris) |>
  addIoLayer(type = "rangeBar", color = "#4E79A7", label = "Mean ± 95% CI",
    transform = "mean_ci",
    mapping = list(x_var = "Species", y_var = "Sepal.Length"),
    options = list(level = 0.95)) |>
  defineCategoricalAxis(xAxis = TRUE)

Regression Composite

One-call scatter + trend + CI + R²:

myIO(data = mtcars) |>
  addIoLayer(type = "regression", label = "MPG vs Weight",
    mapping = list(x_var = "wt", y_var = "mpg"),
    options = list(method = "lm", showCI = TRUE, showStats = TRUE))

Moving Average

df <- data.frame(x = 1:100, y = cumsum(rnorm(100)))

myIO(data = df) |>
  addIoLayer(type = "line", color = "#CCCCCC", label = "Raw",
    mapping = list(x_var = "x", y_var = "y")) |>
  addIoLayer(type = "line", color = "#E15759", label = "SMA-10",
    transform = "smooth",
    mapping = list(x_var = "x", y_var = "y"),
    options = list(method = "sma", window = 10))

Use method = "ema" with alpha for exponential moving average.

Transform + Type Compatibility

Not every transform works with every chart type. The table below shows valid combinations:

Type Supported Transforms
"line" "identity", "lm", "loess", "polynomial", "smooth"
"point" "identity", "mean", "summary", "residuals"
"bar" "identity", "mean", "summary"
"area" "identity", "ci"
"rangeBar" "identity", "mean_ci"
"groupedBar" "identity"
"histogram" "identity"
"hexbin" "identity"
"treemap" "identity"
"donut" "identity"
"gauge" "identity"
"heatmap" "identity"
"candlestick" "identity"
"waterfall" "identity", "cumulative"
"sankey" "identity"
"boxplot" "identity"
"violin" "identity"
"ridgeline" "identity"
"text" "identity"

If you pass an incompatible combination, addIoLayer() will error with a clear message.

Theming

Use setTheme() to customize chart appearance with CSS custom properties.

Basic Theming

myIO() |>
  addIoLayer(
    type = "point",
    color = "#56B4E9",
    label = "scatter",
    data = mtcars,
    mapping = list(x_var = "wt", y_var = "mpg")
  ) |>
  setTheme(
    text_color = "#e0e0e0",
    grid_color = "#333333",
    bg = "#1a1a2e",
    font = "Fira Code, monospace"
  )

Theme Parameters

Parameter CSS Property Description
text_color --chart-text-color Axis labels and tick text
grid_color --chart-grid-color Grid and axis lines
bg --chart-bg Chart background color
font --chart-font Font family

Custom Properties

Pass additional CSS custom properties via ... (the chart- prefix is added automatically):

myIO() |>
  addIoLayer(
    type = "bar",
    color = "coral",
    label = "bars",
    data = mtcars,
    mapping = list(x_var = "cyl", y_var = "mpg")
  ) |>
  defineCategoricalAxis(xAxis = TRUE) |>
  setTheme(
    bg = "#fafafa",
    text_color = "#333",
    "border-radius" = "8px"
  )

Dark Mode Example

Combine theming with color choices for a dark-mode chart:

aq <- airquality
aq$Month <- paste0("M", aq$Month)

myIO() |>
  addIoLayer(
    type = "line",
    color = c("#00d2ff", "#ff6b6b", "#feca57", "#48dbfb", "#ff9ff3"),
    label = "Month",
    data = aq,
    mapping = list(x_var = "Day", y_var = "Temp", group = "Month")
  ) |>
  setTheme(
    text_color = "#b0b0b0",
    grid_color = "#2d2d2d",
    bg = "#0d1117",
    font = "Inter, system-ui, sans-serif"
  )

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